Providing a mask image

ABSTRACT

A method and system for providing a mask image including receiving medical image data including a temporal dimension and generating a frequency data set including data points with in each case one frequency value by applying a Fourier transform to the image data. The Fourier transform is applied at least along the temporal dimension. The frequency data set is segmented into two sub-areas based on at least a frequency threshold value. The mask image is generated by applying an inverse Fourier transform to the first and/or the second sub-area of the frequency data set. The generated mask image is provided.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of DE 102020205762.1, filed on May7, 2020 which is hereby incorporated by reference in its entirety.

FIELD

Embodiments relates to a method for providing a mask image and to amethod for providing a differential image.

BACKGROUND

X-ray-based subtraction methods are frequently applied for detection ofa change over time at a body region of an examination object, forexample a movement of a medical object at the body region. A change overtime at the body region of the examination object may include, forexample a spreading movement of a contrast medium in a vessel systemand/or a movement of a surgical and/or diagnostic instrument.

With the X-ray-based subtraction methods, two X-ray images acquired inchronological sequence, that map the same body region, areconventionally subtracted from each other. The elements in the X-rayimages that are irrelevant and/or disruptive to a treatment and/ordiagnosis, which elements, for example, do not vary over time, arereduced.

With methods such as digital subtraction angiography (DSA), theacquisition is often different in two phases. In a first phase, the maskphase, conventionally at least one X-ray image with optimum imagequality, for example maximum X-ray radiation dose, is acquired. In asecond phase, the fill phase, conventionally at least one second X-rayimage is acquired. A change at the examined body region of theexamination object has taken place at this instant. A plurality ofsecond X-ray images is frequently successively acquired in a shortchronological sequence for a detection of this change over time at thebody region. The change over time at the body region may then berendered visible by a subtraction of one X-ray image from the firstphase and one of the second X-ray images from the second phase. Theknown DSA methods are frequently based on the assumption of a uniformmovement of different tissue regions and bony structures in the bodyregion. A DSA method often cannot be applied in the case of a differencebetween the respective movements. In addition, the examination object isadversely exposed to a high X-ray radiation dose in the mask phase.

Furthermore, image processing algorithms exist, that frequently amplifyassociated spatial frequencies in medical image data in order tohighlight medical objects and/or specific anatomical structures, forexample a vessel section, and/or attenuate spatial frequencies ofinterfering structures, for example a bone structure. Disadvantageouslythe medical objects and/or anatomical structures to be highlighted areoften attenuated or the interfering structures are amplified, however,for example in fields of view where mutual overlaying occurs.

BRIEF SUMMARY AND DESCRIPTION

The scope of the present invention is defined solely by the appendedclaims and is not affected to any degree by the statements within thissummary. The present embodiments may obviate one or more of thedrawbacks or limitations in the related art.

Embodiments provide masking of image fractions of different movementstates.

Embodiments include a method, for example a computer-implemented method,for providing a mask image. In a first step a), medical image dataincluding a temporal dimension is received. Furthermore, in a secondstep b), a frequency data set including data points with in each caseone frequency value is generated by applying a Fourier transform to theimage data. The Fourier transform is applied at least along the temporaldimension. In a third step c), the frequency data set is segmented intotwo sub-areas on the basis of at least one frequency threshold value.The mask image is generated in a fourth step d) by applying an inverseFourier transform to the first and/or the second sub-area of thefrequency data set. In a fifth step e), the mask image is provided.

The above-described steps a) to e) may be carried out successivelyand/or at least partially simultaneously.

Receiving the image data in step a) may include, for example, capturingand/or reading a computer-readable data memory and/or receiving from adata memory unit, for example a database. Furthermore, the image datamay be provided by a provision unit of a medical imaging device.

The image data may be spatially resolved, for example, two-dimensionallyand/or three-dimensionally. In addition, the image data is temporallyresolved. the image data may map at least partially a joint examinationregion of an examination object. The examination object may be, forexample, a human and/or animal patient and/or a phantom. The image datamay include, for example, X-ray images, for example projection images,and/or ultrasound images and/or computed tomography images and/ormagnetic resonance images and/or positron emission tomography images.The image data may map the examination region at different instants,moreover, for example acquisition instants. Consequently, the image datais resolved both spatially and temporally. This may ensure that a changeover time at the examination region, for example a spreading movement ofa contrast medium and/or a movement of a medical and/or anatomicalobject, is mapped in the image data. Furthermore, the image data mayinclude metadata, wherein the metadata may include, for example,information relating to an acquisition parameter and/or operatingparameter of the medical imaging device.

For example, the image data may include a plurality of individualimages. The image data, for example the individual images, may include aplurality of image points, for example pixels and/or voxels. In eachcase an image point of the image data may include a time intensity curvealong the temporal dimension.

The Fourier transform for generating the frequency data set in step b)may include, for example, a windowed Fourier transform, for example ashort-time Fourier transform (STFT), and/or a wavelet transform. Thewindowed Fourier transform may include, for example, a rectangularfunction and/or a Hanning window function and/or a Gaussian windowfunction. Furthermore, the windowed Fourier transform may be implementedas a fast Fourier transform (FFT). The frequency data set may begenerated by applying the Fourier transform to the spatially andtemporally resolved image data at least along the temporal dimension.The data points of the frequency data set may correspond to one imagepoint in each case of the image data. For example, the frequency dataset may also be spatially resolved. Furthermore, the data points of thefrequency data set may each include frequency information relating tothe time intensity curve of the respectively corresponding image pointof the image data. The frequency information of one data point in eachcase may include, for example, a frequency spectrum relating to the timeintensity curve of the image point corresponding therewith. Whenapplying a short-time Fourier transform to the image data this mayinclude a window function for classification of the image data, forexample of time intensity curves of the image points, in time segmentsalong the temporal dimension. The time segments in corresponding regionsof the frequency spectrum of the frequency data set may be transferredto the image data by applying the short-time Fourier transform.transitions, for example transition frequencies, between differentmovement states, that are mapped in the image data in a spatiallyresolved manner, may be reliably and precisely determined in thefrequency data set hereby, for example by an analysis of the frequencyspectrum.

A resolution along the temporal dimension and a frequency resolution inthe respective time segments or the regions of the frequency spectrumcorresponding therewith are optimized by applying a wavelet transform tothe image data for generation of the frequency data set. This is madepossible, for example, by a simultaneous shifting and scaling of thewindow function in the case of the wavelet transform. For example,applying the short-time Fourier transform and/or the wavelet transformin step b) provides, for example continuous, adjusting of the timesegments or the regions of the frequency spectrum correspondingtherewith. This is advantageous, for example, for repeated performanceof steps a) to e).

In step c), the frequency data set may be segmented into two sub-areason the basis of at least one frequency threshold value. The frequencythreshold value may be specified, for example, as a transition frequencybetween two different movement states. The movement states may eachinclude a different movement speed. The frequency values may describe achange over time in the time intensity curves of the image points of theimage data. In addition, the segmenting may include a classification ofsub-areas, for example data points, of the frequency data set whosefrequency value, for example a frequency mean, is in each case less thanand/or more than the frequency threshold value. For example, thefrequency data set may be classified by the segmenting into a firstsub-area including frequency values less than and/or equal to thefrequency threshold value and a second sub-area including frequencyvalues more than and/or equal to the frequency threshold value. The datapoints of the first sub-area may correspond, for example, to imagepoints of the image data, that map static and/or slow-changing sectionsof the examination region. Furthermore, the data points of the secondsub-area may correspond to image points of the image data, that mapcomparatively fast-changing sections of the examination region. Thedifference between the static and/or slow-changing sections and thecomparatively fast-changing sections may be based on the frequencythreshold value. For example, the classification of the frequency dataset into the first and the second sub-areas may be set by the frequencythreshold value.

In step d), the mask image may be generated by applying an inverseFourier transform to the first and/or the second sub-area of thefrequency data set. On an application of the inverse Fourier transformto the first and the second sub-areas of the frequency data set thefrequency values of at least one of the sub-areas may be adjusted instep c). If the frequency data set was generated by applying a wavelettransform in step b), the inverse Fourier transform in step d) mayinclude, for example, a wavelet synthesis. The mask image includes aplurality of image points. Each image point of the mask imagecorresponds to a data point of the frequency data set. The mask imagemay include, for example, the same or a lower dimensionality than theimage data. the mask image may include masked and unmasked fields ofview. The image points of the respective field of view may correspond tothe data points of one sub-area in each case of the segmented frequencydata set.

On generation of the mask image by applying the inverse Fouriertransform to the first sub-area of the frequency data set the imagepoints of the unmasked field of view of the mask image may correspond tothe data points of the first sub-area of the frequency data set. Theunmasked fields of view of the mask image may correspond, for example,to image points of the image data, therefore, that image points mapstatic and/or slow-changing sections of the examination region.Furthermore, the masked fields of view of the mask image may correspond,for example, to image points of the image data, that image points mapcomparatively fast-changing sections of the examination region.

Analogously thereto the image points of the unmasked field of view ofthe mask image correspond to the data points of the second sub-area ofthe frequency data set if the mask image is generated by applying theinverse Fourier transform to the second sub-area of the frequency dataset.

Furthermore, the mask image may include a plurality of individual maskimages. The individual mask images correspond in each case to oneindividual image of the image data. Furthermore, generating the maskimage in step d) may include an, for example adaptive and/or a weighted,averaging of the individual mask images, for example along the temporaldimension. The averaging mask image generated in the process may beprovided, for example, as the mask image in step e).

Providing the mask image in step e) may include, for example, storing ona computer-readable storage medium and/or displaying on a presentationunit and/or transferring to a provision unit. For example, a graphicalrepresentation of the mask image on the presentation unit may bedisplayed. The mask image may be provided, for example, for a method forproviding a differential image.

The method provides a classification, for example masking, of imagefractions of different movement states based on an analysis, for examplea comparison, of frequency values, which frequency values are obtainedfrom the image data by Fourier transform. The acquisition of a maskimage may be omitted hereby, so an overall duration of the examinationand/or an exposure of the examination object, for example due to anX-ray dose, may be reduced. In addition, varying, for exampleanatomical, structures and/or medical objects, that are mapped in theimage data, may be retained, for example in contrast to anintensity-based masking.

In an embodiment, the inverse Fourier transform may be applied in stepd) to the first and the second sub-areas of the frequency data set. Thefrequency values of at least one of the sub-areas may be adjusted, forexample before application of the inverse Fourier transform.

Adjusting the frequency values may include, for example, filling the atleast one sub-area with predetermined, for example constant, frequencyvalues. The at least one sub-area, for example the frequency values ofthe at least one sub-area, may be filled, for example, with zeros (zerofilling). Alternatively, or in addition, the frequency values of the atleast one sub-area may be filled and/or adjusted, for example scaled,according to a specified distribution function, for example a windowfunction and/or attenuation function. This may provide the applicationof the inverse Fourier transform to the first and the second sub-areasof the frequency data set while retaining the segmenting. Furthermore,the mask image may be generated on the basis of the adjusted frequencyvalues of the at least one sub-area in such a way that it has theunmasked and masked fields of view according to the segmenting in stepc).

In an embodiment, the Fourier transform may also be applied in step b)along at least one spatial axis. Segmenting of the frequency data set instep c) may be based on at least one first frequency threshold value inrespect of a temporal frequency and at least one second frequencythreshold value in respect of a spatial frequency.

The Fourier transform in step b) may be applied, for example, in astaggered and/or multi-dimensional manner. For example, the Fouriertransforms may be successively applied to the image data along thetemporal dimension and along the at least one spatial axis.Alternatively, or in addition, the Fourier transform may include amultidimensional design along the temporal dimension and along the atleast one spatial axis. The inverse Fourier transform in step d) may beconfigured analogously to the Fourier transform. For example, theFourier transform may be applied along at least one, for example all, ofthe spatial dimensions of the image data.

Applying the Fourier transform additionally along the at least onespatial axis, the frequency values of the data points of the frequencydata set may describe both a temporal and a spatial change in intensityof the image points of the image data. For example, the frequency valuesof the data points of the frequency data set may include a timefrequency and a spatial frequency in each case, for example as tuples.The first frequency threshold value may be specified in respect of thetemporal frequency and the second frequency threshold value in respectof the spatial frequency. The comparison condition for segmenting thefrequency data set in step c) may include in each case an adequate or anecessary criterion in respect of the first and of the second frequencythreshold value.

In one configuration, the comparison condition for segmenting mayinclude an adequate criterion. The frequency data set may be classifiedby segmenting into a first sub-area including frequency values less thanand/or equal to the first frequency threshold value or less than and/orequal to the second frequency threshold value and in a second sub-areaincluding frequency values more than and/or equal to the first frequencythreshold value and more than and/or equal to the second frequencythreshold value. The data points of the first sub-area may correspond,for example, to image points of the image data, that image points maptemporally or spatially static and/or temporally or spatiallyslow-changing sections of the examination region. Furthermore, the datapoints of the second sub-area correspond to image points of the imagedata, that image points temporally and spatially map fast-changingsections of the examination region.

In another configuration, the comparison condition for segmenting mayinclude a necessary criterion. The frequency data set may be classifiedby segmenting into a first sub-area including frequency values less thanand/or equal to the first frequency threshold value and less than and/orequal to the second frequency threshold value and in a second sub-areaincluding frequency values more than and/or equal to the first frequencythreshold value or more than and/or equal to the second frequencythreshold value. The data points of the first sub-area may correspond,for example, to image points of the image data, that image points maptemporally and spatially static and/or temporally and spatiallyslow-changing sections of the examination region. Furthermore, the datapoints of the second sub-area correspond to image points of the imagedata, that image points temporally or spatially map fast-changingsections of the examination region.

Temporal and spatial changes, for example a movement, in the examinationregion, that are mapped in the image data, may be segmented herebyaccording to the specification of the first and the second frequencythreshold values, for example the first and/or the second sub-area maybe segmented according to an ellipsoid. The difference between the firstfrequency threshold value in respect of the temporal frequency and thesecond frequency threshold value in respect of the spatial frequency mayspecify a type of temporal change to be segmented, for example amovement direction and/or a change in intensity in the case of a flow ofcontrast medium.

In an embodiment, the method may also include steps a2) and a3). In stepa2), a medical object and/or an anatomical structure may be identifiedin the image data. Furthermore, in step a3), data points may bedetermined in the frequency data set, which points correspond to theidentified medical object and/or the anatomical structure. Thecorresponding data points may be excluded from the segmenting in stepc). Steps a2) and a3) may be carried out, for example, after step a) andbefore step b) of the method.

The medical object may be configured, for example, as a surgical and/ordiagnostic instrument, for example a catheter and/or guide wire and/orendoscope. In addition, the medical object may be configured as acontrast medium, for example a contrast medium bolus, arranged in theexamination region. Furthermore, the anatomical structure may include,for example a vessel structure, for example a vessel section, and/or anorgan, for example a hollow organ, and/or a tissue boundary. Forexample, the medical object may be arranged at least partially in theanatomical structure.

Identifying the medical object and/or the anatomical structure in theimage data in step a2) may include, for example, segmenting a mapping ofthe medical object and/or the anatomical structure in the image data.Segmenting of the medical object and/or the anatomical structure may bebased, for example, on a comparison of the image values of the imagepoints with a specified threshold value. Alternatively, or in addition,the medical object and/or the anatomical structure may be identified,for example, on the basis of a form, for example a contour.Alternatively, or in addition, the medical object and/or the anatomicalstructure may be identified on the basis of at least one markerstructure, which marker structure is mapped in the image data.Furthermore, identifying the medical object and/or the anatomicalstructure may include annotating image points of the image data, forexample by way of a user input. In step a2), for example the imagepoints may be identified in the image data, which image points map themedical object and/or the anatomical structure.

In step a3), the data points may be identified in the frequency dataset, which data points correspond to the image points in the image dataidentified in step a2). This may take place, for example, on the basisof a mapping rule between the image points of the image data and thedata points of the frequency data set when applying the Fouriertransform.

the corresponding data points may be excluded from the segmenting instep c). The fields of view, for example the image points, that wereidentified in step a3), may be specified as unmasked fields of view forthe mask image. The medical object and/or the anatomical structureidentified in step a2) is retained as an unmasked field of view on anapplication, for example subtraction and/or multiplication, of the maskimage.

Steps a2) and a3) may be carried out after step a) and before step b).

In an embodiment, a subset of data points of the frequency data setaround the corresponding data points may also be determined in step a3),which subset is excluded from the segmenting in step c).

The subset of data points of the frequency data set around thecorresponding data points may be determined, for example, on the basisof a distribution function, for example a spatial one. Alternatively, orin addition, the subset may include the data points of the frequencydata set, which data points are located within a specified spatialdistance from the corresponding data points. Alternatively, or inaddition, a subset of image points may be determined whose image pointsare located within the specified spatial distance from the image pointsidentified in step a2). Hereafter the corresponding data points may bedetermined on the basis of the identified image points and the subset ofdata points on the basis of the subset of image points. A safety margin,for example a spatial one, may be determined around the image pointshereby, that map the medical object and/or the anatomical structure,which margin is excluded from the segmenting and for example from asubsequent masking. The sub-area of the data points of the frequencydata set may be specified analogously to the corresponding data pointsas an unmasked field of view for the mask image. The mapping of themedical object and/or the anatomical structure may be retained hereby,for example completely, as an unmasked field of view also onapplication, for example subtraction and/or multiplication, of the maskimage.

In an embodiment, step a) may also include receiving an object parameterand/or a structure parameter. The object parameter may includeinformation relating to the medical object and/or the structureparameter information relating to the anatomical structure. Furthermore,the corresponding data points may be determined in step a3) on the basisof the object parameter and/or the structure parameter.

Receiving the object parameter and/or the structure parameter mayinclude, for example, capturing and/or reading a computer-readable datamemory and/or receiving from a data memory unit, for example a database.Furthermore, the object parameter and/or the structure parameter may beprovided by a provision unit of a medical imaging device. Alternatively,or in addition, the object parameter and/or the structure parameter maybe identified on the basis of a user input at an input unit.

The object parameter may include information relating to the medicalobject, for example at least one operating parameter and/or a materialproperty and/or a form property and/or information relating to a markerstructure arranged on the medical object. The structure parameter mayalso include information relating to anatomical structure, for example atissue parameter and/or a physiological parameter and/or informationrelating to a marker structure arranged on the anatomical structureand/or information relating to a contrast medium arranged in theanatomical structure. Furthermore, the structure parameter may includegeometric information, for example a central line and/or a volumenetwork model and/or spatial spread information, relating to theanatomical structure. The image points of the image data, that imagepoints map the medical object and/or the anatomical structure, may beidentified reliably and in a computationally efficient manner hereby.For example, after identification of at least part of the medical objectand/or the anatomical structure the remaining mapping may be identifiedon the basis of the object parameter and/or the structure parameter, forexample by virtual completion. Hereafter, corresponding data points ofthe frequency data set may be identified on the basis of the identifiedimage points of the image data.

Alternatively, or in addition, the corresponding data points of thefrequency data set may be identified on the basis of a comparison of therespective frequency values with the object parameter and/or thestructure parameter.

Determining the corresponding data points in step a3) based on theobject parameter and/or the structure parameter may provide reliably andin a simultaneously computationally efficient manner that the mapping ofthe medical object and/or the anatomical structure is retained also onapplication, for example a subtraction and/or multiplication, of themask image.

In an embodiment, step a) may also include registering the medical imagedata, for example the individual images. The image data, for example theindividual images, may be registered relative to each other along thetemporal dimension. Registering the image data may include a rigidand/or non-rigid transformation of the individual images, for examplerelative to a reference individual image and/or relative to each other.Alternatively, or in addition, registering the image data may include amovement correction, for example based on a physiological movementsignal of the examination object. A deviation of the individual imagesrelative to each other, for example due to a movement of the examinationobject, may be reduced hereby. the accuracy and reliability of thesegmenting of the frequency data set in step c) may be improved hereby.

In an embodiment, the medical image data may map at least partially ajoint examination region of an examination object. Step a) may alsoinclude receiving a physiological signal and/or a movement signal of theexamination object. In addition, the at least one frequency thresholdvalue may be specified in step c) on the basis of the physiologicalsignal and/or the movement signal.

Receiving the physiological signal and/or the movement signal mayinclude, for example, capturing and/or reading a computer-readable datamemory and/or receiving from a data memory unit, for example a database.Furthermore, the physiological signal and/or the movement signal may beprovided by a provision unit of a medical imaging device and/or by asensor unit for monitoring the examination object.

The physiological signal may include, for example, a heart signal, forexample a pulse signal, and/or a respiratory signal of the examinationobject. Furthermore, the movement signal may include spatially andtemporally resolved movement information of at least part of theexamination object, for example of the examination region. The frequencythreshold value may be specified on the basis of the physiologicalsignal and/or the movement signal in such a way that the frequencyvalues of the data points of the frequency data set, that correspond toa change over time, for example a movement, of the examination region,that change over time at least partially follows the physiologicalsignal and/or the movement signal, are more than and/or equal to thefrequency threshold value. The fields of view of the image data, thatmap the change over time of the examination region, may correspond to anunmasked field of view of the mask image hereby. This may provide that achange over time at the examination region, which change over time atleast partially follows the physiological signal and/or the movementsignal, is retained also on an application, for example subtractionand/or multiplication, of the mask image.

Embodiments include a method for providing a differential image. In afirst step s1), medical image data including a temporal dimension isacquired by a medical imaging device. In a second step s2), a mask imageis received by applying an embodiment of the method for providing a maskimage to the medical image data. In a third step s3), the differentialimage is generated by subtracting and/or multiplying the mask image andthe medical image data. The differential image is provided in a fourthstep s4).

The advantages of the method for providing a differential imagesubstantially correspond to the advantages of the method for providing amask image. Features, advantages or alternative embodiments mentioned inthis connection may likewise be transferred to the other claimed subjectmatters and vice versa.

The medical imaging device for acquisition of the medical image data instep s1) may be configured, for example, as a medical X-ray device, forexample C-arm X-ray device, and/or as a computed tomography system (CT)and/or as a sonography device and/or as a positron emission tomographysystem (PET). The medical image data acquired in step s1) may also beprovided for step a) of the method for providing a mask image.Generating the differential image in step s3) may include a subtractionand/or multiplication, for example image point-wise and/or individualimage-wise, of the mask image by the medical image data. Masked fieldsof view of the mask image may be removed from the medical image datahereby. The differential image has the unmasked fields of view of themask image.

Providing the differential image in step s4) may include, for example,storing on a computer-readable storage medium and/or displaying on apresentation unit and/or transferring to a provision unit. For example,a graphical representation of the differential image may be displayed onthe presentation unit.

Embodiments provide a provision unit including an arithmetic unit, amemory unit and an interface. A provision unit may be configured tocarry out the above-described methods for providing a mask image and/orfor providing a differential image, and their aspects. The provisionunit is configured to carry out the methods and their aspects in thatthe interface, memory unit and arithmetic unit are configured to carryout the corresponding method steps.

For example, the interface may be configured for carrying out steps a),a2), a3) and/or e) of the method for providing a mask image.Furthermore, the interface may be configured for carrying out steps s2)and s4) of the method for providing a differential image. Furthermore,the arithmetic unit and/or the memory unit may be configured forcarrying out the remaining steps of the method.

The advantages of the provision unit substantially correspond to theadvantages of the method for providing a mask image and/or for providinga differential image. Features, advantages or alternative embodimentsmentioned in this connection may likewise be transferred to the otherclaimed subject matters and vice versa.

Embodiments provide a medical imaging device including a provision unit.The medical imaging device, for example the provision unit, isconfigured for carrying out a method for providing a mask image and/orfor providing a differential image. For example, the medical imagingdevice may be configured as a medical X-ray device, for example C-armX-ray device, and/or as a computed tomography system (CT) and/or as asonography device and/or as a positron emission tomography system (PET).The medical imaging device may also be configured for acquisition and/orfor receiving and/or for providing the medical image data and/or themask image and/or the differential image.

The advantages of the medical imaging device substantially correspond tothe advantages of the method for providing a mask image and/or forproviding a differential image. Features, advantages or alternativeembodiments mentioned in this connection may likewise be transferred tothe other claimed subject matters and vice versa.

Embodiments provide a computer program product with a computer program,that may be loaded directly into a memory of a provision unit, withprogram segments in order to carry out all steps of the method forproviding a mask image and/or for providing a differential image whenthe program segments are executed by the provision unit. The computerprogram product may include software with a source code, that still hasto be compiled and linked or that only has to be interpreted, or anexecutable software code, that for execution only has to be loaded intothe provision unit. As a result of the computer program product themethod for providing a mask image and/or the method for providing adifferential image by a provision unit may be carried out quickly,repeatedly in an identical manner and robustly. The computer programproduct is configured such that it may carry out the method steps by theprovision unit.

The computer program product is stored, for example, on acomputer-readable storage medium or saved on a network or server fromwhere it may be loaded into the processor of a provision unit, that maybe directly connected to the provision unit or be configured as part ofthe provision unit. Furthermore, control information of the computerprogram product may be stored on an electronically readable datacarrier. The control information of the electronically readable datacarrier may be configured in such a way that it carries out a methodwhen the data carrier is used in a provision unit. Examples ofelectronically readable data carriers are a DVD, a magnetic tape or aUSB stick on which electronically readable control information, forexample software, is stored. When this control information is read fromthe data carrier and stored in a provision unit, embodiments of theabove-described method may be carried out.

Embodiments may also start from a computer-readable storage mediumand/or electronically readable data carrier on which program segments,that may be read and executed by a provision unit, are stored in orderto carry out all steps of the method for providing a mask image and/orfor providing a differential image when the program segments areexecuted by the provision unit.

An implementation in terms of software includes the advantage thatpreviously used provision units may also be easily retrofitted by way ofa software update in order to work. Apart from the computer program acomputer program product of this kind may optionally include additionalelements, such as documentation and/or additional components, as well ashardware components, such as hardware keys (dongles, etc.) in order touse the software.

BRIEF DESCRIPTION OF THE FIGURES

FIGS. 1, 2, 3, and 4 depict schematic representations of differentembodiments of a method for providing a mask image.

FIG. 5 depicts a schematic representation of an embodiment of a methodfor providing a differential image.

FIG. 6 depicts a schematic representation of a provision unit accordingto an embodiment.

FIG. 7 depicts a schematic representation of a medical C-arm X-raydevice as an example of a medical imaging device according to anembodiment.

DETAILED DESCRIPTION

FIG. 1 schematically represents an embodiment of the method forproviding a mask image. In a first step a), medical image data IDincluding a temporal dimension may be received REC-ID. Furthermore, in asecond step b), a frequency data set FD including data points with ineach case one frequency value may be generated GEN-FD by applying aFourier transform to the image data ID. The Fourier transform may beapplied at least along the temporal dimension. In a third step c), thefrequency data set FD may be segmented SEG-FD into two sub-areas TB1 andTB2 on the basis of at least one frequency threshold value FTH. Thesegmented frequency data set SFD may be provided in the process. In afourth step d), the mask image MI may be generated GEN-MI by applying aninverse Fourier transform to the first TB1 and/or the second sub-areaTB2 of the frequency data set FD. Furthermore, the mask image MI may beprovided PROV-MI in a fifth step e).

The Fourier transform for generating GEN-FD the frequency data set FD instep b) may include, for example, a windowed Fourier transform, forexample a short-time Fourier transform (STFT), and/or a wavelettransform. The windowed Fourier transform may include, for example, arectangular function and/or a Hanning window function and/or a Gaussianwindow function. Furthermore, the windowed Fourier transform may beimplemented as a fast Fourier transform (FFT).

Furthermore, the frequency data set FD may be classified by thesegmenting SEG-FD into the first sub-area TB1 including frequency valuesless than and/or equal to the at least one frequency threshold value FTHand a second sub-area TB2 including frequency values more than and/orequal to the frequency threshold value FTH. The data points of the firstsub-area TB1 may correspond, for example, to image points of the imagedata ID, that map static and/or slow-changing sections of theexamination region UB, for example a bone structure. Furthermore, thedata points of the second sub-area TB2 may correspond to image points ofthe image data ID, that map comparatively fast-changing sections of theexamination region UB, for example a medical object arranged thereinand/or a contrast medium and/or a moved anatomical structure.

the mask image MI may include masked and unmasked fields of view. Theimage points of the respective field of view may correspond to the datapoints of one sub-area TB1, TB2 in each case of the segmented frequencydata set SFD.

With a generation GEN-MI of the mask image MI by applying the inverseFourier transform to the first sub-area TB1 of the frequency data set FDthe image points of the unmasked field of view of the mask image MI maycorrespond to the data points of the first sub-area TB1 of the frequencydata set FD. The unmasked fields of view of the mask image MI maycorrespond, for example, to image points of the image data ID, that maptemporally static and/or slow-changing sections of the examinationregion UB, therefore. Furthermore, the masked fields of view of the maskimage MI may correspond, for example, to image points of the image dataID, that map comparatively fast-changing sections of the examinationregion UB.

Furthermore, the inverse Fourier transform may be applied in step d) forexample to the first TB1 and the second sub-area TB2 of the segmentedfrequency data set SFD, with the frequency values of at least one ofsub-areas TB1 and/or TB2 being adjusted.

In addition, the Fourier transform in step b) may be applied along atleast one spatial axis. Segmenting of the frequency data set SEG-FD instep c) may be based on at least one first frequency threshold value inrespect of a temporal frequency and at least one second frequencythreshold value in respect of a spatial frequency.

FIG. 2 depicts a schematic representation of an embodiment of the methodfor providing a mask image PROV-MI. The method may also include stepsa2) and a3). Step a2) may include identifying a medical object ID-MOand/or an anatomical structure ID-AS in the image data ID. Furthermore,in step a3), corresponding data points may be identified in thefrequency data set FD, that correspond to the identified medical objectand/or the anatomical structure. The corresponding data points may beexcluded from the segmenting SEG-FD in step c).

Furthermore, in step a3), a subset of data points of the frequency dataset FD around the corresponding data points may also be determined,which subset is excluded from the segmenting SEG-FD in step c).

FIG. 3 schematically represents an embodiment of the method forproviding a mask image PROV-MI. Step a) may also include receiving anobject parameter REC-OP and/or a structure parameter REC-SP. The objectparameter OP may include information relating to the medical object.Furthermore, the structure parameter SP may include information relatingto the anatomical structure. the corresponding data points may bedetermined in step a3) on the basis of the object parameter OP and/orthe structure parameter SP.

FIG. 4 depicts a schematic representation of an embodiment of the methodfor providing a mask image PROV-MI. Step a) may also include registeringthe medical image data REG-ID. Registered medical image data RID may beprovided hereafter for step a2) and/or step b). The medical image dataID, for example along the temporal dimension including a plurality ofindividual images, may map a joint examination region of an examinationobject. Step a) may also include receiving REC-SIG a physiologicalsignal and/or a movement signal SIG of the examination object.Furthermore, the at least one frequency threshold value FTH may bespecified DET-FTH in step c) on the basis of the physiological signaland/or the movement signal SIG.

FIG. 5 schematically represents an embodiment of the method forproviding a differential image. In a first step s1), the medical imagedata ID including a temporal dimension may be acquired ACQ-ID by amedical imaging device. Furthermore, the medical image data ID may beprovided PROV-ID for step a) of the method V1 for providing a mask imagePROV-MI. By applying the method V1 for providing a mask image PROV-MI tothe image data ID, the mask image MI may be received REC-MI in step s2).Hereafter in a step s3), the differential image DI may be generatedGEN-DI by subtracting and/or multiplying the mask image MI by themedical image data ID. In a fourth step s4), the differential image DImay be provided PROV-DI.

FIG. 6 schematically depicts a provision unit PRVS including aninterface IF, an arithmetic unit CU and a memory unit MU. The provisionunit PRVS may be configured to carry out a method for providing a maskimage PROV-MI and/or a method for providing a differential image PROV-DIin that the interface IF, the arithmetic unit CU and the memory unit MUare configured to carry out the corresponding method steps.

The interface IF may be configured for carrying out steps a), a2), a3)and/or e) of the method for providing a mask image PROV-MI. Furthermore,the interface IF may be configured for carrying out steps s2) and s4) ofthe method for providing a differential image PROV-DI. Furthermore, thearithmetic unit CU and/or the memory unit MU may be configured forcarrying out the remaining steps of the method.

The provision unit PRVS may be, for example, a computer, amicrocontroller or an integrated circuit. Alternatively, the provisionunit PRVS may be a real or virtual group of computers (an Englishtechnical term for a real group is a “Cluster”, an English technicalterm for a virtual group is a “Cloud”). The provision unit PRVS may alsobe configured as a virtual system, that is run on a real computer or areal or virtual group of computers (virtualization).

An interface IF may be a hardware or software interface (for example,PCI bus, USB or Firewire). An arithmetic unit CU may include hardwareelements or software elements, for example a microprocessor or what isknown as an FPGA (English acronym for “Field Programmable Gate Array”).A memory unit MU may be implemented as a non-permanent working memory(Random Access Memory, RAM for short) or as a permanent mass memory(hard disk, USB stick, SD card, Solid State Disk).

The interface IF may include, for example, a plurality ofsub-interfaces, that carry out the different steps of the respectivemethod. In other words, the interface IF may also be conceived as alarge number of interfaces IF. The arithmetic unit CU may include, forexample, a plurality of sub-arithmetic units, that carry out differentsteps of the respective method. In other words, the arithmetic unit CUmay also be conceived as a large number of arithmetic units CU.

FIG. 7 schematically represents a medical C-arm X-ray device 37 as anexample of a medical imaging device. the medical C-arm X-ray device 37may include a provision unit PRVS, for example for control of themedical C-arm X-ray device 37. The medical C-arm X-ray device 37, forexample the provision unit PRVS, is configured to carry out a method forproviding a mask image PROV-MI and/or a method for providing adifferential image PROV-DI.

The medical C-arm X-ray device 37 also includes a detector unit 34 andan X-ray source 33. For acquisition of the medical image data ID the arm38 of the C-arm X-ray device 37 may be mounted to move around one ormore axes. Furthermore, the medical C-arm X-ray device 37 may include amovement apparatus 39, that provides a movement of the C-arm X-raydevice 37 in the space, for example a wheel system and/or rail system.

For acquisition of the medical image data ID from the examination regionUB of the examination object 31 arranged on a patient supportingfacility 32 the provision unit PRVS may send a signal 24 to the X-raysource 33. The X-ray source 33 may then emit an X-ray beam bundle, forexample a cone beam and/or fan beam and/or parallel beam. When the X-raybeam bundle, after an interaction with the examination region UB of theexamination object 31 to be mapped, strikes a surface of the detectorunit 34, the detector unit 34 may send a signal 21 to the provision unitPRVS. The provision unit PRVS may receive REC-ID the medical image dataID, for example on the basis of the signal 21.

Furthermore, the medical C-arm X-ray device 37 may include an input unit42, for example a keyboard, and/or a presentation unit 41, for example amonitor and/or display. The input unit 42 may be integrated in thepresentation unit 41, for example in the case of a capacitive and/orresistive input display. An input by an operator at the input unit 42may provide, for example supplementary, control of the medical C-armX-ray device 37, for example of a method. For this, the input unit 42may send, for example, a signal 26 to the provision unit PRVS.

Furthermore, the presentation unit 41 may be configured to displayinformation and/or graphical representations of information of the C-armX-ray device 37 and/or the provision unit PRVS and/or furthercomponents. For this, the provision unit PRVS may send, for example, asignal 25 to the presentation unit 41. For example, the presentationunit 41 may be configured for display of a graphical representation ofthe medical image data ID and/or the frequency data set FD and/or themask image MI and/or the differential image DI.

The schematic representations contained in the described figures do notdepict any kind of scale or size ratio.

In conclusion, reference is made once again to the fact that the methodsdescribed in detail above and the represented apparatuses are merelyembodiments, that may be modified in a wide variety of ways by a personskilled in the art without departing from the scope of the invention.Furthermore, use of the indefinite article “a” or “an” does not precludethe relevant features from also being present several times. Similarly,the terms “unit” and “element” do not preclude the relevant componentsfrom consisting of a plurality of cooperating sub-components, that mayoptionally also be spatially distributed.

It is to be understood that the elements and features recited in theappended claims may be combined in different ways to produce new claimsthat likewise fall within the scope of the present invention. Thus,whereas the dependent claims appended below depend from only a singleindependent or dependent claim, it is to be understood that thesedependent claims may, alternatively, be made to depend in thealternative from any preceding or following claim, whether independentor dependent, and that such new combinations are to be understood asforming a part of the present specification.

While the present invention has been described above by reference tovarious embodiments, it may be understood that many changes andmodifications may be made to the described embodiments. It is thereforeintended that the foregoing description be regarded as illustrativerather than limiting, and that it be understood that all equivalentsand/or combinations of embodiments are intended to be included in thisdescription.

1. A method for providing a mask image, the method comprising: receivingmedical image data including a temporal dimension; generating afrequency data set including one or more data points with in each caseone frequency value by applying a Fourier transform to the medical imagedata, wherein the Fourier transform is applied at least along thetemporal dimension; segmenting the frequency data set into a firstsub-area and a second sub-area based on at least a frequency thresholdvalue; generating the mask image by applying an inverse Fouriertransform to the first sub-area, the second sub-area, or the firstsub-area and the second sub-area of the frequency data set; andproviding the mask image.
 2. The method of claim 1, wherein generatingthe mask image comprises applying the inverse Fourier transform to thefirst sub-area and the second sub-area of the frequency data set,wherein frequency values of at least one of either the first sub-area orthe second sub-area is adjusted.
 3. The method of claim 1, whereingenerating the frequency data set comprises applying the Fouriertransform along at least one spatial axis, wherein segmenting of thefrequency data set is based on at least one first frequency thresholdvalue in respect of a temporal frequency and at least one secondfrequency threshold value in respect of a spatial frequency.
 4. Themethod of claim 1, further comprising: identifying a medical object, ananatomical structure, or the medical object and the anatomical structurein the medical image data; and determining data points corresponding tothe identified medical object and/or the anatomical structure in thefrequency data set, wherein corresponding data points are excluded fromsegmentation.
 5. The method of claim 4, wherein a subset of data pointsof the frequency data set around the corresponding data points is alsodetermined, wherein the subset of data points is excluded fromsegmentation.
 6. The method of claim 4, wherein identifying furthercomprises receiving an object parameter, a structure parameter, or theobject parameter and the structure parameter, wherein the objectparameter includes information relating to the medical object, whereinthe structure parameter includes information relating to the anatomicalstructure, wherein the corresponding data points are determined on thebasis of the object parameter, the structure parameter, or the objectparameter and the structure parameter.
 7. The method of claim 1, whereinreceiving medical image data further comprises registering the medicalimage data.
 8. The method of claim 1, wherein the medical image datamaps at least partially a joint examination region of an examinationobject, wherein receiving the medical image data further comprisesreceiving a physiological signal, a movement signal, or thephysiological signal and the movement signal of the examination object;wherein the at least one frequency threshold value is specified on thebasis of the physiological signal, the movement signal, or thephysiological signal and the movement signal of the examination object.9. The method of claim 1, further comprising: generating a differentialimage by subtracting, multiplying, or subtracting and multiplying themask image and the medical image data; and providing the differentialimage.
 10. A system comprising: a medical imaging device configured toacquire medical image data including a temporal dimension; a provisionunit configured to generate a frequency data set including one or moredata points with in each case one frequency value by applying a Fouriertransform to the medical image data, wherein the Fourier transform isapplied at least along the temporal dimension, the provision unitfurther configured to segment the frequency data set into a firstsub-area and a second sub-area based on at least a frequency thresholdvalue and generate a mask image by applying an inverse Fourier transformto the first sub-area, the second sub-area, or the first sub-area andthe second sub-area of the frequency data set.
 11. The system of claim10, wherein the provision unit is configured to generate the mask imageby applying the inverse Fourier transform to the first sub-area and thesecond sub-area of the frequency data set, wherein frequency values ofat least one of either the first sub-area or the second sub-area isadjusted.
 12. The system of claim 10, wherein the provision unit isconfigured to generating the frequency data set comprises applying theFourier transform along at least one spatial axis, wherein segmenting ofthe frequency data set is based on at least one first frequencythreshold value in respect of a temporal frequency and at least onesecond frequency threshold value in respect of a spatial frequency. 13.The system of claim 10, wherein the provision unit is further configuredto generate a differential image by subtracting, multiplying, orsubtracting and multiplying the mask image and the medical image data.14. A provision unit comprising: a processor; and a memory coupled tothe processor, the memory configured to store machine-readableinstructions executable by the processor, the machine-readableinstructions comprising instructions to: receive medical image dataincluding a temporal dimension; generate a frequency data set includingone or more data points with in each case one frequency value byapplying a Fourier transform to the medical image data, wherein theFourier transform is applied at least along the temporal dimension;segment the frequency data set into a first sub-area and a secondsub-area based on at least a frequency threshold value; generate a maskimage by applying an inverse Fourier transform to the first sub-area,the second sub-area, or the first sub-area and the second sub-area ofthe frequency data set; and provide the mask image.
 15. The provisionunit of claim 14, wherein the machine-readable instructions to generatethe mask image comprise machine-readable instructions to apply theinverse Fourier transform to the first sub-area and the second sub-areaof the frequency data set, wherein frequency values of at least one ofeither the first sub-area or the second sub-area are adjusted.
 16. Theprovision unit of claim 14, wherein the machine-readable instructions togenerate the frequency data set comprise machine-readable instructionsto apply the Fourier transform along at least one spatial axis, whereinsegmenting of the frequency data set is based on at least one firstfrequency threshold value in respect of a temporal frequency and atleast one second frequency threshold value in respect of a spatialfrequency.
 17. The provision unit of claim 14, wherein themachine-readable instructions further comprise instructions to: identifya medical object, an anatomical structure, or the medical object and theanatomical structure in the medical image data; and determine datapoints corresponding to the identified medical object and/or theanatomical structure in the frequency data set, wherein correspondingdata points are excluded from segmentation.
 18. The provision unit ofclaim 17, machine-readable instructions further comprise instructions todetermine a subset of data points of the frequency data set around thecorresponding data points, wherein the subset of data points is excludedfrom segmentation.
 19. The provision unit of claim 17, wherein themachine-readable instructions for identifying further comprisesinstructions to receive an object parameter, a structure parameter, orthe object parameter and the structure parameter, wherein the objectparameter includes information relating to the medical object, whereinthe structure parameter includes information relating to the anatomicalstructure, wherein the corresponding data points are determined on thebasis of the object parameter, the structure parameter, or the objectparameter and the structure parameter.
 20. The provision unit of claim14, wherein the machine-readable instructions further compriseinstructions to: generate a differential image by subtracting,multiplying, or subtracting and multiplying the mask image and themedical image data; and provide the differential image.